Search Results for author:
Found 12 papers, 5 papers with code
Seed-ASR: Understanding Diverse Speech and Contexts with LLM-based Speech Recognition
Modern automatic speech recognition (ASR) model is required to accurately transcribe diverse speech signals (from different domains, languages, accents, etc) given the specific contextual information in various application scenarios.
Ranked #2 on
Speech Recognition
on AISHELL-1
(using extra training data)
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Seed-TTS: A Family of High-Quality Versatile Speech Generation Models
Seed-TTS offers superior controllability over various speech attributes such as emotion and is capable of generating highly expressive and diverse speech for speakers in the wild.
StemGen: A music generation model that listens
End-to-end generation of musical audio using deep learning techniques has seen an explosion of activity recently.
InstructME: An Instruction Guided Music Edit And Remix Framework with Latent Diffusion Models
We tested InstructME in instrument-editing, remixing, and multi-round editing.
Differentiable Wavetable Synthesis
Differentiable Wavetable Synthesis (DWTS) is a technique for neural audio synthesis which learns a dictionary of one-period waveforms i. e. wavetables, through end-to-end training.
Supervised Chorus Detection for Popular Music Using Convolutional Neural Network and Multi-task Learning
This paper presents a novel supervised approach to detecting the chorus segments in popular music.
GiantMIDI-Piano: A large-scale MIDI dataset for classical piano music
In this article, we create a GiantMIDI-Piano (GP) dataset containing 38, 700, 838 transcribed notes and 10, 855 unique solo piano works composed by 2, 786 composers.
ByteSing: A Chinese Singing Voice Synthesis System Using Duration Allocated Encoder-Decoder Acoustic Models and WaveRNN Vocoders
This paper presents ByteSing, a Chinese singing voice synthesis (SVS) system based on duration allocated Tacotron-like acoustic models and WaveRNN neural vocoders.
ClariNet: Parallel Wave Generation in End-to-End Text-to-Speech
In this work, we propose a new solution for parallel wave generation by WaveNet.
Neural Voice Cloning with a Few Samples
Speaker adaptation is based on fine-tuning a multi-speaker generative model with a few cloning samples.
Supervised Speech Separation Based on Deep Learning: An Overview
A more recent approach formulates speech separation as a supervised learning problem, where the discriminative patterns of speech, speakers, and background noise are learned from training data.
Exploring Neural Transducers for End-to-End Speech Recognition
In this work, we perform an empirical comparison among the CTC, RNN-Transducer, and attention-based Seq2Seq models for end-to-end speech recognition.
